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Energies 2016, 9(9), 727;

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Computer Engineering Department, Faculty of Computer and Information Sciences, University of Sakarya, 2nd Ring Street, Esentepe Campus, Serdivan, Sakarya 54187, Turkey
Author to whom correspondence should be addressed.
Academic Editor: Francisco Martínez-Álvarez
Received: 13 May 2016 / Revised: 25 July 2016 / Accepted: 6 September 2016 / Published: 8 September 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
PDF [2194 KB, uploaded 8 September 2016]


Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey’s natural gas consumption increased as well in parallel with the world‘s over the last decade. This consumption growth in Turkey has led to the formation of a market structure for the natural gas industry. This significant increase requires additional investments since a rise in consumption capacity is expected. One of the reasons for the consumption increase is the user-based natural gas consumption influence. This effect yields imbalances in demand forecasts and if the error rates are out of bounds, penalties may occur. In this paper, three univariate statistical methods, which have not been previously investigated for mid-term year-ahead monthly natural gas forecasting, are used to forecast natural gas demand in Turkey’s Sakarya province. Residential and low-consumption commercial data is used, which may contain seasonality. The goal of this paper is minimizing more or less gas tractions on mid-term consumption while improving the accuracy of demand forecasting. In forecasting models, seasonality and single variable impacts reinforce forecasts. This paper studies time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA) methods. Here, 2011–2014 monthly data were prepared and divided into two series. The first series is 2011–2013 monthly data used for finding seasonal effects and model requirements. The second series is 2014 monthly data used for forecasting. For the ARIMA method, a stationary series was prepared and transformation process prior to forecasting was done. Forecasting results confirmed that as the computation complexity of the model increases, forecasting accuracy increases with lower error rates. Also, forecasting errors and the coefficients of determination values give more consistent results. Consequently, when there is only consumption data in hand, all methods provide satisfying results and the differences between each method is very low. If a statistical software tool is not used, time series decomposition, the most primitive method, or Winters exponential smoothing requiring little mathematical knowledge for natural gas demand forecasting can be used with spreadsheet software. A statistical software tool containing ARIMA will obtain the best results. View Full-Text
Keywords: demand forecasting; natural gas; univariate methods; time series decomposition; Holt-Winters model; autoregressive integrated moving average (ARIMA); seasonal ARIMA demand forecasting; natural gas; univariate methods; time series decomposition; Holt-Winters model; autoregressive integrated moving average (ARIMA); seasonal ARIMA

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Akpinar, M.; Yumusak, N. Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods. Energies 2016, 9, 727.

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